Various sensing systems currently exist for performing collision warning and countermeasure system operations, such as detection, classification, tracking, and relative distance and velocity estimation of objects within a close proximity of a host vehicle.
Collision warning and countermeasure system operations include providing a vehicle operator knowledge and awareness of vehicles and objects that are within a close proximity of the host vehicle to prevent colliding with those objects. The countermeasure systems exist in various passive and active forms. Some countermeasure systems are used to aid in prevention of a collision, others are used to aid in the prevention of injury to a vehicle operator.
Certain collision warning and countermeasure systems are able to sense an object within close proximity of the host vehicle and warn the host vehicle operator, such that the operator can take precautionary steps to prevent a collision or injury. Other collision warning and countermeasure systems activate passive or active countermeasures. Passive countermeasures may, for example, include the activation of airbags or load limiting seatbelts. An example active countermeasure is the activation of brake control, whereby, the system itself aids in preventing a collision or injury.
Active safety technologies depend on the ability of the sensor systems to identify potential objects in the path of the host vehicle and to provide a threat assessment so that appropriate actions may be performed. A threat assessment system typically performs tasks, such as path prediction, object detection, quantification of threat posed by the detected objects, and selection of the objects that are of a concern or that pose a significant threat.
The success of the action in preventing a collision or injury to a vehicle occupant is related to the proper functioning of each task of the threat assessment system. In particular, path prediction and object threat assessment are key elements in that success. Path prediction has been accomplished using various methods, such as methods that use a Kalman filter or that assume that the travel path of an object is in the shape of an arc. The radius of the arc is determined in response to the relative yaw rate and speed of the objects relative to the host vehicle.
Object threat assessment may be affected by several different factors including, relative deceleration levels, range, range rate, speed, and position of the objects. Object threat assessment may also be affected by whether an object is a stationary object or a moving object, size of the object, traffic scenario, road conditions, driver experience, risk aversion and mood, and weather conditions.
To obtain information regarding all of the above-mentioned factors that can affect object threat assessment many sensors are needed and a large amount of information is gathered that needs to be processed. Thus, such a threat assessment system that is capable of obtaining the stated information is complex, costly, and infeasible for mass production. Also, gathering and processing of the mentioned information is time consuming and can negatively affect response time available to avoid a collision. As known in the art, time is of the essence in preventing a collision.
Thus, there exists a need for an improved threat assessment system that is accurate and minimizes the amount of information to be processed to avoid a collision and the amount processing time involved therein.